Optimizing Combination Cancer Therapy Based on Single Cell Analysis

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Single-cell mass cytometry measuring ~40 markers per cell is ordered into a Spade minimum spanning tree that recovers developmental branches, then populations are auto-gated by a CAST decision-tree to produce homogeneous subpopulations (thousands of cells each). Intracellular perturbation responses are summarized by a Bayesian probability matrix and assembled into a nested effects model for drug combinations that scores minimal drug sets; validated on an ovarian (HeLa-derived) cell line with TRAIL plus three kinase inhibitors, predicted 2– and 3‑drug rankings match survival assays.

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" chapters (17)

1. 0:00Introduction to signaling
2. 1:33Single-cell technologies
3. 3:46Spade algorithm development
4. 6:04Spade methodology
5. 8:32Validation of Spade
6. 10:16Inferences in cancer
7. 11:24Other analysis algorithms
8. 13:09Perturbation analysis
9. 14:48Signaling map applications
10. 16:35Clinical relevance
11. 18:39Cast algorithm
12. 20:04Improving signaling robustness
13. 22:06Optimal drug combination
14. 23:17Graphical models
15. 25:51HeLa cell case study
16. 26:57Data-driven results
17. 29:58Conclusion and Q&A